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3D perception, especially point cloud classification, has achieved substantial progress.
ImageNet: A Large-Scale Hierarchical Image Database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
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State of the art in surface reconstruction from point clouds
Berger, M., Tagliasacchi, A., Seversky, L., Alliez, P., Levine, J., Sharf, A., and Silva, C · 2014
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Discrete signal processing on graphs: Frequency analysis
Sandryhaila, A. and Moura, J. M · 2014
Earlier work this paper cites.
3d shapenets: A deep representation for volumetric shapes
Wu, Z., Song, S., Khosla, A., Yu, F., Zhang, L., Tang, X., and Xiao, J · 2015
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Volumetric and multi-view cnns for object classification on 3d data
Qi, C. R., Su, H., Nießner, M., Dai, A., Yan, M., and Guibas, L. J · 2016
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Dynamic edge-conditioned filters in convolutional neural networks on graphs
Simonovsky, M. and Komodakis, N · 2017
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Graph signal processing: Overview, challenges, and applications
Ortega, A., Frossard, P., Kovačević, J., Moura, J. M., and Vandergheynst, P · 2018
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mixup: Beyond empirical risk minimization
Zhang, H., Cissé, M., Dauphin, Y. N., and Lopez-Paz, D · 2018
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Objectnet: A large-scale bias-controlled dataset for pushing the limits of object recognition models
Barbu, A., Mayo, D., Alverio, J., Luo, W., Wang, C., Gutfreund, D., Tenenbaum, J., and Katz, B · 2019
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Clusternet: Deep hierarchical cluster network with rigorously rotation-invariant representation for point cloud analysis
Chen, C., Li, G., Xu, R., Chen, T., Wang, M., and Lin, L · 2019
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Benchmarking neural network robustness to common corruptions and perturbations
Hendrycks, D. and Dietterich, T. G · 2019
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Relation-shape convolutional neural network for point cloud analysis
Liu, Y., Fan, B., Xiang, S., and Pan, C · 2019
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Do imagenet classifiers generalize to imagenet?
Recht, B., Roelofs, R., Schmidt, L., and Shankar, V · 2019
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Revisiting point cloud classification: A new benchmark dataset and classification model on real-world data
Uy, M. A., Pham, Q., Hua, B., Nguyen, D. T., and Yeung, S · 2019
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Dynamic graph cnn for learning on point clouds
Wang, Y., Sun, Y., Liu, Z., Sarma, S. E., Bronstein, M. M., and Solomon, J. M · 2019
Cited alongside, same era.
Squeezesegv2: Improved model structure and unsupervised domain adaptation for road-object segmentation from a lidar point cloud
Wu, B., Zhou, X., Zhao, S., Yue, X., and Keutzer, K · 2019
Cited alongside, same era.
A fourier perspective on model robustness in computer vision
Yin, D., Lopes, R. G., Shlens, J., Cubuk, E. D., and Gilmer, J · 2019
Cited alongside, same era.
Cutmix: Regularization strategy to train strong classifiers with localizable features
Yun, S., Han, D., Oh, S. J., Chun, S., Choe, J., and Yoo, Y · 2019
Cited alongside, same era.
Rotation invariant convolutions for 3d point clouds deep learning
Zhang, Z., Hua, B.-S., Rosen, D. W., and Yeung, S.-K · 2019
Cited alongside, same era.
Dup-net: Denoiser and upsampler network for 3d adversarial point clouds defense
Vector neurons: A general framework for so (3)-equivariant networks
Deng, C., Litany, O., Duan, Y., Poulenard, A., Tagliasacchi, A., and Guibas, L. J · 2021
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Revisiting point cloud shape classification with a simple and effective baseline
Goyal, A., Law, H., Liu, B., Newell, A., and Deng, J · 2021
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Point cloud augmentation with weighted local transformations
Kim, S., Lee, S., Hwang, D., Lee, J., Hwang, S. J., and Kim, H. J · 2021
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Regularization strategy for point cloud via rigidly mixed sample
Lee, D., Lee, J., Lee, J., Lee, H., Lee, M., Woo, S., and Lee, S · 2021
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Pointguard: Provably robust 3d point cloud classification
Liu, H., Jia, J., and Gong, N. Z · 2021
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Cloud transformers: A universal approach to point cloud processing tasks
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Zhou, H., Chen, K., Zhang, W., Fang, H., Zhou, W., and Yu, N · 2019
Cited alongside, same era.
Pointmixup: Augmentation for point clouds
Chen, Y., Hu, V. T., Gavves, E., Mensink, T., Mettes, P., Yang, P., and Snoek, C. G. M · 2020
Cited alongside, same era.
Self-robust 3d point recognition via gather-vector guidance
Dong, X., Chen, D., Zhou, H., Hua, G., Zhang, W., and Yu, N · 2020
Cited alongside, same era.
Orderly disorder in point cloud domain
Ghahremani, M., Tiddeman, B., Liu, Y., and Behera, A · 2020
Cited alongside, same era.
Pct: Point cloud transformer, 2020
Guo, M.-H., Cai, J.-X., Liu, Z.-N., Mu, T.-J., Martin, R. R., and Hu, S.-M · 2020
Cited alongside, same era.
Pointaugment: An auto-augmentation framework for point cloud classification
Li, R., Li, X., Heng, P., and Fu, C · 2020
Cited alongside, same era.
Robustpointset: A dataset for benchmarking robustness of point cloud classifiers
Taghanaki, S. A., Luo, J., Zhang, R., Wang, Y., Jayaraman, P. K., and Jatavallabhula, K. M · 2020
Cited alongside, same era.
Mazur, K. and Lempitsky, V · 2021
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Common objects in 3d: Large-scale learning and evaluation of real-life 3d category reconstruction
Reizenstein, J., Shapovalov, R., Henzler, P., Sbordone, L., Labatut, P., and Novotny, D · 2021
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Adversarially robust 3d point cloud recognition using self-supervisions
Sun, J., Cao, Y., Choy, C., Yu, Z., Anandkumar, A., Mao, Z. M., and Xiao, C · 2021
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Unsupervised point cloud pre-training via occlusion completion
Wang, H., Liu, Q., Yue, X., Lasenby, J., and Kusner, M. J · 2021
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Walk in the cloud: Learning curves for point clouds shape analysis
Xiang, T., Zhang, C., Song, Y., Yu, J., and Cai, W · 2021
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Triangle-net: Towards robustness in point cloud learning
Xiao, C. and Wachs, J. P · 2021
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Point-bert: Pre-training 3d point cloud transformers with masked point modeling
Yu, X., Tang, L., Rao, Y., Huang, T., Zhou, J., and Lu, J · 2021
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Point transformer
Zhao, H., Jiang, L., Jia, J., Torr, P. H., and Koltun, V · 2021
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